MNE-Python includes a general framework for cluster-based tests to allow for performing arbitrary sets of contrasts along arbitrary dimensions while controlling for multiple comparisons. In practice, this means that the code is designed to work with many forms of data, whether they are stored as SourceEstimate for source-space data, or as Evoked for sensor-space data, or even as custom data formats, as necessary for time–frequency data. It can operate on any NumPy array using the natural (grid) connectivity structure, or a more complex connectivity structure (such as those in a brain source space) with help of a sparse adjacency matrix. MNE-Python also facilitates the use of methods for variance control, such as the “hat” method (Ridgway et al., 2012). Two common use cases are provided in Figure 7.
